OpenRaiser/Intern-Atlas huggingface dataset:
Using the Crowd to Generate Content for Scenario-Based Serious-Games
Venue: arXiv cs.AI 2014
Year: 2014

In the last decade, scenario-based serious-games have become a main tool for learning new skills and capabilities. An important factor in the development of such systems is the overhead in time, cost and human resources to manually create the content for these scenarios. We focus on how to create content for scenarios in medical, military, commerce and gaming applications where maintaining the integrity and coherence of the content is integral for the system's success. To do so, we present an automatic method for generating content about everyday activities through combining computer science techniques with the crowd. We use the crowd in three basic ways: to capture a database of scenarios of everyday activities, to generate a database of likely replacements for specific events within that scenario, and to evaluate the resulting scenarios. We found that the generated scenarios were rated as reliable and consistent by the crowd when compared to the scenarios that were originally captured. We also compared the generated scenarios to those created by traditional planning techniques. We found that both methods were equally effective in generated reliable and consistent scenarios, yet the main advantages of our approach is that the content we generate is more varied and much easier to create. We have begun integrating this approach within a scenario-based training application for novice investigators within the law enforcement departments to improve their questioning skills.
A logical model of Theory of Mind for virtual agents in the context of job interview simulation
Venue: arXiv cs.AI 2014
Year: 2014

Job interview simulation with a virtual agents aims at improving people's social skills and supporting professional inclusion. In such simulators, the virtual agent must be capable of representing and reasoning about the user's mental state based on social cues that inform the system about his/her affects and social attitude. In this paper, we propose a formal model of Theory of Mind (ToM) for virtual agent in the context of human-agent interaction that focuses on the affective dimension. It relies on a hybrid ToM that combines the two major paradigms of the domain. Our framework is based on modal logic and inference rules about the mental states, emotions and social relations of both actors. Finally, we present preliminary results regarding the impact of such a model on natural interaction in the context of job interviews simulation.
Expressing social attitudes in virtual agents for social training games
Venue: arXiv cs.HC 2014
Year: 2014

The use of virtual agents in social coaching has increased rapidly in the last decade. In order to train the user in different situations than can occur in real life, the virtual agent should be able to express different social attitudes. In this paper, we propose a model of social attitudes that enables a virtual agent to reason on the appropriate social attitude to express during the interaction with a user given the course of the interaction, but also the emotions, mood and personality of the agent. Moreover, the model enables the virtual agent to display its social attitude through its non-verbal behaviour. The proposed model has been developed in the context of job interview simulation. The methodology used to develop such a model combined a theoretical and an empirical approach. Indeed, the model is based both on the literature in Human and Social Sciences on social attitudes but also on the analysis of an audiovisual corpus of job interviews and on post-hoc interviews with the recruiters on their expressed attitudes during the job interview.
Real-time Automatic Emotion Recognition from Body Gestures
Venue: arXiv cs.HC 2014
Year: 2014

Although psychological research indicates that bodily expressions convey important affective information, to date research in emotion recognition focused mainly on facial expression or voice analysis. In this paper we propose an approach to realtime automatic emotion recognition from body movements. A set of postural, kinematic, and geometrical features are extracted from sequences 3D skeletons and fed to a multi-class SVM classifier. The proposed method has been assessed on data acquired through two different systems: a professionalgrade optical motion capture system, and Microsoft Kinect. The system has been assessed on a "six emotions" recognition problem, and using a leave-one-subject-out cross validation strategy, reached an overall recognition rate of 61.3% which is very close to the recognition rate of 61.9% obtained by human observers. To provide further testing of the system, two games were developed, where one or two users have to interact to understand and express emotions with their body.
Towards an Intelligent Framework for Pressure-based 3D Curve Drawing
Venue: arXiv cs.HC 2014
Year: 2014

Pen pressure is an input channel typically available in tablet pen device. To date, little attention has been paid to the use of pressure in the domain of graphical interaction, its usage largely limited to drawing and painting program, typically for varying brush characteristic such as stroke width, opacity and color. In this paper, we explore the use of pressure in 3D curve drawing. The act of controlling pressure using pen, pencil and brush in real life appears effortless, but to mimic this natural ability to control pressure using a pressure sensitive pen in the realm of electronic medium is difficult. Previous pressure based interaction work have proposed various signal processing techniques to improve the accuracy in pressure control, but a one-for-all signal processing solution tend not to work for different curve types. We propose instead a framework which applies signal processing techniques tuned to individual curve type. A neural network classifier is used as a curve classifier. Based on the classification, a custom combination of signal processing techniques is then applied. Results obtained point to the feasibility and advantage of the approach.
A Survey on Dynamic Job Scheduling in Grid Environment Based on Heuristic Algorithms
Venue: arXiv cs.DC 2014
Year: 2014

Computational Grids are a new trend in distributed computing systems. They allow the sharing of geographically distributed resources in an efficient way, extending the boundaries of what we perceive as distributed computing. Various sciences can benefit from the use of grids to solve CPU-intensive problems, creating potential benefits to the entire society. Job scheduling is an integrated part of parallel and distributed computing. It allows selecting correct match of resource for a particular job and thus increases the job throughput and utilization of resources. Job should be scheduled in an automatic way to make the system more reliable, accessible and less sensitive to subsystem failures. This paper provides a survey on various heuristic algorithms, used for scheduling in grid.
An Analysis of Rank Aggregation Algorithms
Venue: arXiv cs.DS 2014
Year: 2014

Rank aggregation is an essential approach for aggregating the preferences of multiple agents. One rule of particular interest is the Kemeny rule, which maximises the number of pairwise agreements between the final ranking and the existing rankings. However, Kemeny rankings are NP-hard to compute. This has resulted in the development of various algorithms. Fortunately, NP-hardness may not reflect the difficulty of solving problems that arise in practice. As a result, we aim to demonstrate that the Kemeny consensus can be computed efficiently when aggregating different rankings in real case. In this paper, we extend a dynamic programming algorithm originally for Kemeny scores. We also provide details on the implementation of the algorithm. Finally, we present results obtained from an empirical comparison of our algorithm and two other popular algorithms based on real world and randomly generated problem instances. Experimental results show the usefulness and efficiency of the algorithm in practical settings.
On the Power of Multiplexing in Number-on-the-Forehead Protocols
Venue: arXiv cs.CC 2014
Year: 2014

We study the direct-sum problem for $k$-party ``Number On the Forehead'' (NOF) deterministic communication complexity. We prove several positive results, showing that the complexity of computing a function $f$ in this model, on $\ell$ instances, may be significantly cheaper than $\ell$ times the complexity of computing $f$ on a single instance. Quite surprisingly, we show that this is the case for ``most'' (boolean, $k$-argument) functions. We then formalize two-types of sufficient conditions on a NOF protocol $Q$, for a single instance, each of which guarantees some communication complexity savings when appropriately extending $Q$ to work on $\ell$ instances. One such condition refers to what each party needs to know about inputs of the other parties, and the other condition, additionally, refers to the communication pattern that the single-instance protocol $Q$ uses. In both cases, the tool that we use is ``multiplexing'': we combine messages sent in parallel executions of protocols for a single instance, into a single message for the multi-instance (direct-sum) case, by xoring them with each other.
Ergonomic-driven Geometric Exploration and Reshaping
Venue: arXiv cs.GR 2014
Year: 2014

The paper addresses the following problem: given a set of man-made shapes, e.g., chairs, can we quickly rank and explore the set of shapes with respect to a given avatar pose? Answering this question requires identifying which shapes are more suitable for the defined avatar and pose; and moreover, to provide fast preview of how to alter the input geometry to better fit the deformed shapes to the given avatar pose? The problem naturally links physical proportions of human body and its interaction with object shapes in an attempt to connect ergonomics with shape geometry. We designed an interaction system that allows users to explore shape collections using the deformation of human characters while at the same time providing interactive previews of how to alter the shapes to better fit the user-specified character. We achieve this by first mapping ergonomics guidelines into a set of simultaneous multi-part constraints based on target contacts; and then, proposing a novel contact-based deformation model to realize multi-contact constraints. We evaluate our framework on various chair models and validate the results via a small user study.
Rateless-Coding-Assisted Multi-Packet Spreading over Mobile Networks
Venue: arXiv cs.NI 2014
Year: 2014

A novel Rateless-coding-assisted Multi-Packet Relaying (RMPR) protocol is proposed for large-size data spreading in mobile wireless networks. With this lightweight and robust protocol, the packet redundancy is reduced by a factor of $\sqrt n$, while the spreading time is reduced at least by a factor of $\ln (n)$. Closed-form bounds and explicit non-asymptotic results are presented, which are further validated through simulations. Besides, the packet duplication phenomenon in the network setting is analyzed for the first time.
A Generalized Robust Filtering Framework for Nonlinear Differential-Algebraic Systems
Venue: arXiv cs.SY 2014
Year: 2014

A generalized dynamical robust nonlinear filtering framework is established for a class of Lipschitz differential algebraic systems, in which the nonlinearities appear both in the state and measured output equations. The system is assumed to be affected by norm-bounded disturbance and to have both norm-bounded uncertainties in the realization matrices as well as nonlinear model uncertainties. We synthesize a robust H_infty filter through semidefinite programming and strict linear matrix inequalities (LMIs). The admissible Lipschitz constants of the nonlinear functions are maximized through LMI optimization. The resulting H_infty filter guarantees asymptotic stability of the estimation error dynamics with prespecified disturbance attenuation level and is robust against time-varying parametric uncertainties as well as Lipschitz nonlinear additive uncertainty. Explicit bound on the tolerable nonlinear uncertainty is derived based on a norm-wise robustness analysis.
When Agile Is Not Good Enough: an initial attempt at understanding how to make the right decision
Venue: arXiv cs.SE 2014
Year: 2014

Particularly over the last ten years, Agile has attracted not only the praises of a broad range of enthusiast software developers, but also the criticism of others. Either way, adoption or rejection of Agile seems sometimes to be based more on a questionable understanding than on a critical, well-informed decision making process. In this paper, the dual nature of the above criticism is discussed, and the arguments against Agile have been classified within a critical taxonomy of risk factors. A decisional model and tool based on such taxonomy are consequently proposed for supporting software engineers and other stakeholders in the decision-making about whether or not to use Agile. The tool, which is freely available online, comes with a set of guidelines: its purpose is to facilitate the community of software developers to contribute to further assessing the potential and the criticalities of Agile Methods.
Reciprocity in Gift-Exchange-Games
Venue: arXiv cs.AI 2014
Year: 2014

This paper presents an analysis of data from a gift-exchange-game experiment. The experiment was described in `The Impact of Social Comparisons on Reciprocity' by Gächter et al. 2012. Since this paper uses state-of-art data science techniques, the results provide a different point of view on the problem. As already shown in relevant literature from experimental economics, human decisions deviate from rational payoff maximization. The average gift rate was $31$%. Gift rate was under no conditions zero. Further, we derive some special findings and calculate their significance.
Logic Verification of Product-Line Variant Requirements
Venue: arXiv cs.SE 2014
Year: 2014

Formal verification of variant requirements has gained much interest in the software product line (SPL) community. Feature diagrams are widely used to model product line variants. However, there is a lack of precisely defined formal notation for representing and verifying such models. This paper presents an approach to modeling and verifying SPL variant feature diagrams using first-order logic. It provides a precise and rigorous formal interpretation of the feature diagrams. Logical expressions can be built by modeling variants and their dependencies by using propositional connectives. These expressions can then be validated by any suitable verification tool. A case study of a Computer Aided Dispatch (CAD) system variant feature model is presented to illustrate the verification process.
Containment Control for a Social Network with State-Dependent Connectivity
Venue: arXiv cs.SY 2014
Year: 2014

Social interactions influence our thoughts, opinions and actions. In this paper, social interactions are studied within a group of individuals composed of influential social leaders and followers. Each person is assumed to maintain a social state, which can be an emotional state or an opinion. Followers update their social states based on the states of local neighbors, while social leaders maintain a constant desired state. Social interactions are modeled as a general directed graph where each directed edge represents an influence from one person to another. Motivated by the non-local property of fractional-order systems, the social response of individuals in the network are modeled by fractional-order dynamics whose states depend on influences from local neighbors and past experiences. A decentralized influence method is then developed to maintain existing social influence between individuals (i.e., without isolating peers in the group) and to influence the social group to a common desired state (i.e., within a convex hull spanned by social leaders). Mittag-Leffler stability methods are used to prove asymptotic stability of the networked fractional-order system.
Region-based approximation of probability distributions (for visibility between imprecise points among obstacles)
Venue: arXiv cs.CG 2014
Year: 2014

Let $p$ and $q$ be two imprecise points, given as probability density functions on $\mathbb R^2$, and let $\cal R$ be a set of $n$ line segments (obstacles) in $\mathbb R^2$. We study the problem of approximating the probability that $p$ and $q$ can see each other; that is, that the segment connecting $p$ and $q$ does not cross any segment of $\cal R$. To solve this problem, we approximate each density function by a weighted set of polygons; a novel approach to dealing with probability density functions in computational geometry.
Discriminative Functional Connectivity Measures for Brain Decoding
Venue: arXiv cs.AI 2014
Year: 2014

We propose a statistical learning model for classifying cognitive processes based on distributed patterns of neural activation in the brain, acquired via functional magnetic resonance imaging (fMRI). In the proposed learning method, local meshes are formed around each voxel. The distance between voxels in the mesh is determined by using a functional neighbourhood concept. In order to define the functional neighbourhood, the similarities between the time series recorded for voxels are measured and functional connectivity matrices are constructed. Then, the local mesh for each voxel is formed by including the functionally closest neighbouring voxels in the mesh. The relationship between the voxels within a mesh is estimated by using a linear regression model. These relationship vectors, called Functional Connectivity aware Local Relational Features (FC-LRF) are then used to train a statistical learning machine. The proposed method was tested on a recognition memory experiment, including data pertaining to encoding and retrieval of words belonging to ten different semantic categories. Two popular classifiers, namely k-nearest neighbour (k-nn) and Support Vector Machine (SVM), are trained in order to predict the semantic category of the item being retrieved, based on activation patterns during encoding. The classification performance of the Functional Mesh Learning model, which range in 62%-71% is superior to the classical multi-voxel pattern analysis (MVPA) methods, which range in 40%-48%, for ten semantic categories.
Monoidal computer II: Normal complexity by string diagrams
Venue: arXiv cs.LO 2014
Year: 2014

In Monoidal Computer I, we introduced a categorical model of computation where the formal reasoning about computability was supported by the simple and popular diagrammatic language of string diagrams. In the present paper, we refine and extend that model of computation to support a formal complexity theory as well. This formalization brings to the foreground the concept of normal complexity measures, which allow decompositions akin to Kleene's normal form. Such measures turn out to be just those where evaluating the complexity of a program does not require substantially more resources than evaluating the program itself. The usual time and space complexity are thus normal measures, whereas the average and the randomized complexity measures are not. While the measures that are not normal provide important design time information about algorithms, and for theoretical analyses, normal measures can also be used at run time, as practical tools of computation, e.g. to set the bounds for hypothesis testing, inductive inference and algorithmic learning.
Data-Adaptive Reduced-Dimension Robust Beamforming Algorithms
Venue: arXiv cs.IT 2014
Year: 2014

We present low complexity, quickly converging robust adaptive beamformers that combine robust Capon beamformer (RCB) methods and data-adaptive Krylov subspace dimensionality reduction techniques. We extend a recently proposed reduced-dimension RCB framework, which ensures proper combination of RCBs with any form of dimensionality reduction that can be expressed using a full-rank dimension reducing transform, providing new results for data-adaptive dimensionality reduction. We consider Krylov subspace methods computed with the Powers-of-R (PoR) and Conjugate Gradient (CG) techniques, illustrating how a fast CG-based algorithm can be formed by beneficially exploiting that the CG-algorithm diagonalizes the reduced-dimension covariance. Our simulations show the benefits of the proposed approaches.
Repeat Accumulate Based Designs for LDPC Codes on Fading Channels
Venue: arXiv cs.IT 2014
Year: 2014

Irregular repeat-accumulate Root-Check LDPC codes based on Progressive Edge Growth (PEG) techniques for block-fading channels are proposed. The proposed Root-Check LDPC codes are {both suitable for channels under $F = 2, 3$ independent fadings per codeword and} for fast fading channels. An IRA(A) Root-Check structure is devised for $F = 2, 3$ independent fadings. The performance of the new codes is investigated in terms of the Frame Error Rate (FER). Numerical results show that the IRAA LDPC codes constructed by the proposed algorithm {outperform by about 1dB the existing} IRA Root-Check LDPC codes under fast-fading channels.
Permutation trinomials over finite fields with even characteristic
Venue: arXiv cs.IT 2014
Year: 2014

Permutation polynomials have been a subject of study for a long time and have applications in many areas of science and engineering. However, only a small number of specific classes of permutation polynomials are described in the literature so far. In this paper we present a number of permutation trinomials over finite fields, which are of different forms.
No more meta-parameter tuning in unsupervised sparse feature learning
Venue: arXiv cs.LG 2014
Year: 2014

We propose a meta-parameter free, off-the-shelf, simple and fast unsupervised feature learning algorithm, which exploits a new way of optimizing for sparsity. Experiments on STL-10 show that the method presents state-of-the-art performance and provides discriminative features that generalize well.
Automatic Estimation of Live Coffee Leaf Infection based on Image Processing Techniques
Venue: arXiv cs.CV 2014
Year: 2014

Image segmentation is the most challenging issue in computer vision applications. And most difficulties for crops management in agriculture are the lack of appropriate methods for detecting the leaf damage for pests treatment. In this paper we proposed an automatic method for leaf damage detection and severity estimation of coffee leaf by avoiding defoliation. After enhancing the contrast of the original image using LUT based gamma correction, the image is processed to remove the background, and the output leaf is clustered using Fuzzy c-means segmentation in V channel of YUV color space to maximize all leaf damage detection, and finally, the severity of leaf is estimated in terms of ratio for leaf pixel distribution between the normal and the detected leaf damage. The results in each proposed method was compared to the current researches and the accuracy is obvious either in the background removal or damage detection.
A Study on the Influence of Caching: Sequences of Dense Linear Algebra Kernels
Venue: arXiv cs.MS 2014
Year: 2014

It is universally known that caching is critical to attain high- performance implementations: In many situations, data locality (in space and time) plays a bigger role than optimizing the (number of) arithmetic floating point operations. In this paper, we show evidence that at least for linear algebra algorithms, caching is also a crucial factor for accurate performance modeling and performance prediction.